Prediction on the volume of non‐performing loans in Turkey using multivariate adaptive regression splines approach

Abstract

Abstract As the volume of non‐performing loans (NPL) plays an important role in the capacity of providing loans, increasing NPL problem is perceived as one of the major challenges in a bank‐based financial system. In recent years, Turkey has been confronted with this problem. In order to reduce NPL volume, necessary precautions that can contribute to government administrators should be taken. Therefore, the most important predictors on NPL volume were determined by using predictive analytics such as multivariate adaptive regression splines that is found out the optimal parameter effects from high‐dimensional data. The dataset, which is obtained from the Central Bank of the Republic of Turkey and Banking and Regulation Supervision Agency, consists of 14 independent variables from 2005 and 2019. As a result of this study, it is indicated that credits, USD/TL exchange rate, and the unemployment rate are the most statistically significant factors in defining the level of NPL volume.

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